ASPDC: Accelerated SPDC Regularized Empirical Risk Minimization for Ill-Conditioned Problems in Large-Scale Machine Learning

This paper aims to improve the response speed of SPDC (stochastic primal-dual coordinate ascent) in large-scale machine learning, as the complexity of per-iteration of SPDC is not satisfactory. We propose an accelerated stochastic primal-dual coordinate ascent called ASPDC and its further accelerated variant, ASPDC-i. Our proposed ASPDC methods achieve a good balance between low per-iteration computation complexity and fast convergence speed, even when the condition number becomes very large. The large condition number causes ill-conditioned problems, which usually requires many more iterations before convergence and longer per-iteration times in data training for machine learning. We performed experiments on various machine learning problems. The experimental results demonstrate that ASPDC and ASPDC-i converge faster than their counterparts, and enjoy low per-iteration complexity as well.